Junior AI Engineer

Responsibilities:

• Design and implement AI-driven solutions across domains such as LLMs, generative AI, NLP, and computer vision.
• Research, prototype, and evaluate state-of-the-art AI models and architectures for real-world applications.
• Build retrieval-augmented generation (RAG) pipelines and integrate AI models into customer-facing products.
• Work with engineers and product teams to embed AI capabilities into platforms and workflows.
• Optimize AI models for accuracy, scalability, and production efficiency.
• Stay up to date with advancements in generative AI, foundation models, and applied AI frameworks.

Requirements:

• Bachelor's/Master's degree in computer science, artificial intelligence, data science, or a related field.
• 1-2 years of experience in building and deploying AI applications.
• Strong proficiency in Python and AI/ML libraries such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, and Scikit-learn.
• Hands-on experience with LLMs, embeddings, prompt engineering, or fine-tuning foundation models.
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is a plus.
• Understanding of vector databases (Pinecone, Qdrant, Weaviate, FAISS) is desirable.
• Strong problem-solving skills with the ability to translate business needs into applied AI solutions
• Excellent communication and cross-team collaboration skills.

Preferred Qualifications:

• Experience with generative AI (LLMs, diffusion models, multimodal AI).
• Familiarity with RAG and orchestration frameworks (LangChain, LlamaIndex, Haystack).
• Exposure to MLOps/AI Ops practices and CI/CD pipelines for AI workflows.
• Contributions to AI/ML open-source projects.